Spatiotemporal Epidemiology of Cryptosporidiosis in the Republic of Ireland, 2008–2017: development of a space–time “cluster recurrence” index

Approximately 1.5 million individuals in Ontario are supplied by private water wells (private groundwater supplies). Unlike municipal supplies, private well water quality remains unregulated, with owners responsible for testing, treating, and maintaining their own water supplies. The COVID-19 global pandemic and associated non-pharmaceutical interventions (NPIs) have impacted many environmental (e.g., surface water and air quality) and human (e.g., healthcare, transportation) systems over the past 15-months (January 2020 to March 2021). To date, the impact of these interventions on private groundwater systems remains largely unknown. Accordingly, the current study aimed to investigate the impact of a province-wide COVID-19 lockdown (late-March 2020) on health behaviours (i.e., private domestic groundwater sampling) and groundwater quality (via Escherichia coli (E. coli) detection and concentration) in private well water in Ontario, using time-series analyses (seasonal decomposition, interrupted time-series) of a large-spatio-temporal dataset (January 2016 to March 2021; N = 743,200 samples). Findings indicate that lockdown concurred with an immediate (p = 0.015) and sustained (p \u3c 0.001) decrease in sampling rates, equating to approximately 2200 fewer samples received per week post-interruption. Likewise, a slightly decreased E. coli detection rate was observed approximately one month after lockdowns began (p = 0.003), while the proportion of “highly contaminated” samples (i.e., E. coli \u3e 10 CFU/100 mL) was shown to increase within one month (p = 0.02), followed by a sustained decrease for the remainder of the year (May 2020–December 2020). Analyses strongly suggest that COVID-19 interventions resulted in discernible impacts on both well user behaviours and hydrogeological mechanisms. Findings may be used as an evidence-base for assisting policy makers, public health practitioners and private well owners in developing recommendations and mitigation strategies to manage public health risks during extreme and/or unprecedented future events

Approche multidisciplinaire pour la caractérisation d’inondations remarquables : enseignements tirés de neuf évènements en France (1910-2010)

The occurrence of a 100 year flood in the Paris area like the January 1910 flood event is a major issue of concern as its potential economic impacts are today estimated around 30 billion euros. This shows the interest of studying exceptional past flood events for flood risk management. It has recently been confirmed by the European Flood Directive 2007/60/CE, in which article 4 recommends describing the floods that had “significant adverse impacts”. This aspect raises some questions. What does significant adverse impact mean? What is the interest to describe those events? This PhD thesis is focussing on a multidisciplinary approach for characterizing remarkable flood events, term used to qualify the 176 flood events selected during the Preliminary Risk Assessment of 2011 into the French National Historical Flood database (BDHI).An evaluation grid, based on the hydrometeorological aspects of the flood hazard as well as the socio-economic and politic consequences of the flood event, was applied to the set of 176 floods. The results conducted to select 9 remarkable flood events from 1910 to 2010. Monograph studies are presented on each of these case studies and are used to deal with three questions on the interest of studying past flood events: 1/ what are the factors involved in the hazard process leading to a remarkable flood event? ; 2/ does a retrospective analysis helps to understand the main explicative factors of flood mortality? ; 3/ how does the society manage a remarkable flood event?The analysis is especially supplied by the use of mapping which offers some issues to better understanding the different spatio-temporal dynamics and the main factors involved in a remarkable flood event.Le retour d’une inondation identique à celle de la Seine en janvier 1910 est à l’origine de nombreuses inquiétudes pour les autorités publiques, vu l’ampleur des dommages potentiels, estimés aujourd’hui à 30 milliards d’euros. Si l’information relative aux inondations historiques fait l’objet depuis plusieurs décennies d’une exploitation pour la gestion du risque, son intérêt s’est récemment vu affirmé par la Directive Inondation 2007/60/CE. L’article 4 de première étape de la Directive, relative à l’Évaluation Préliminaire des Risques d’Inondation ou EPRI, recommande aux gestionnaires de procéder à « la description des inondations survenues dans le passé et ayant eu des impacts négatifs significatifs […] ». Cette injonction permet de s’interroger quant à la signification de ces impacts significatifs, ainsi qu’à l’intérêt de l’étude des inondations du passé. Ce travail de thèse a porté sur la mise au point d’une approche multidisciplinaire pour caractériser des inondations dites remarquables, terme utilisé pour qualifier les 176 évènements de l’EPRI dans la BDHI (Base de Données Historiques sur les Inondations). Une grille multicritères, portant à la fois sur les caractéristiques hydrométéorologiques de l’aléa et les conséquences socio-économiques et politiques des événements, a été appliquée sur les 176 évènements d’inondation (1770-2010). Neuf événements remarquables ont été retenus sur la période 1910-2010 et fait l’objet d’études monographiques présentées en annexe. Ces synthèses détaillées d’évènement d’inondation ont permis d’alimenter la réflexion autour de trois questions transversales : 1/ Quels sont les facteurs impliqués dans le processus d’aléa d’une inondation remarquable ? ; 2/ Une analyse rétrospective permet-elle de mettre en lumière les causes de la mortalité lors d’inondation remarquables ? ; 3/ De quelle manière la société gère-t-elle un évènement remarquable d’inondation ? L’analyse de ces questions s’est en particulier reposée sur la cartographie qui offre des perspectives intéressantes pour restituer les différentes échelles spatio-temporelles impliquées dans l’évènement d’inondation

Behavioral Pathways to Private Well Risk Mitigation: A Structural Equation Modeling Approach

Complex, multi hazard risks such as private groundwater contamination necessitate multiannual risk reduction actions including seasonal, weather-based hazard evaluations. In the Republic of Ireland (ROI), high rural reliance on unregulated private wells renders behavior promotion a vital instrument toward safeguarding household health from waterborne infection. However, to date, pathways between behavioral predictors remain unknown while latent constructs such as extreme weather event (EWE) risk perception and self-efficacy (perceived behavioral competency) have yet to be sufficiently explored. Accordingly, a nationwide survey of 560 Irish private well owners was conducted, with structural equation modeling (SEM) employed to identify underlying relationships determining key supply management behaviors. The pathway analysis (SEM) approach was used to model three binary outcomes: information seeking, post-EWE action, and well testing behavior. Upon development of optimal models, perceived self-efficacy emerged as a significant direct and/or indirect driver of all three behavior types—demonstrating the greatest indirect effect (β=−0.057) on adoption of post-EWE actions and greatest direct (β = 0.222) and total effect (β = 0.245) on supply testing. Perceived self-efficacy inversely influenced EWE risk perception in all three models but positively influenced supply awareness (where present). Notably, the presence of a vulnerable (infant and/or elderly) household member negatively influenced adoption of post-EWE actions (β = −0.131, p = 0.016). Results suggest that residential and age-related factors constitute key demographic variables influencing risk mitigation and are strongly mediated by cognitive variables—particularly self-efficacy. Study findings may help contextualize predictors of private water supply management, providing a basis for future risk-based water interventions

Modelling COVID‑19 Severity In the Republic of Ireland Using Patient Co‑morbidities, Socioeconomic Profle and Geographic Location, February to November 2020

Understanding patient progression from symptomatic COVID-19 infection to a severe outcome represents an important tool for improved diagnoses, surveillance, and triage. A series of models have been developed and validated to elucidate hospitalization, admission to an intensive care unit (ICU) and mortality in patients from the Republic of Ireland. This retrospective cohort study of patients with laboratory-confirmed symptomatic COVID-19 infection included data extracted from national COVID-19 surveillance forms (i.e., age, gender, underlying health conditions, occupation) and geographically-referenced potential predictors (i.e., urban/rural classification, socio-economic profile). Generalised linear models and recursive partitioning and regression trees were used to elucidate COVID-19 progression. The incidence of symptomatic infection over the study-period was 0.96% (n= 47,265), of whom 3781 (8%) required hospitalisation, 615 (1.3%) were admitted to ICU and 1326 (2.8%) died. Models demonstrated an increasingly efficacious ft for predicting hospitalization [AUC 0.816 (95% CI 0.809, 0.822)], admission to ICU [AUC 0.885 (95% CI 0.88 0.89)] and death [AUC of 0.955 (95% CI 0.951 0.959)]. Severe obesity (BMI ≥ 40) was identified as a risk factor across all prognostic models; severely obese patients were substantially more likely to receive ICU treatment [OR 19.630] or die [OR 10.802]. Rural living was associated with an increased risk of hospitalization (OR 1.200 (95% CI 1.143–1.261)]. Urban living was associated with ICU admission [OR 1.533 (95% CI 1.606–1.682)]. Models provide approaches for predicting COVID-19 prognoses, allowing for evidence-based decision making pertaining to targeted non-pharmaceutical interventions, risk-based vaccination priorities and improved patient triage

Psychological Impairment and Extreme Weather Event (EWE) Exposure, 1980–2020: A Global Pooled Analysis Integrating Mental Health and Well-being Metrics

Extreme Weather Events (EWEs) impose a substantial health and socio-economic burden on exposed populations. Projected impacts on public health, based on increasing EWE frequencies since the 1950s, alongside evidence of human-mediated climatic change represents a growing concern. To date, the impacts of EWEs on mental health remain ambiguous, largely due to the inherent complexities in linking extreme weather phenomena with psychological status. This exploratory investigation provides a new empirical and global perspective on the psychological toll of EWEs by exclusively focusing on psychological morbidity among individuals exposed to such events. Morbidity data collated from a range of existing psychological and well-being measures have been integrated to develop a single (“holistic”) metric, namely, psychological impairment. Morbidity, and impairment, were subsequently pooled for key disorders-, specifically PTSD, anxiety and depression. A “composite” (any impairment) post-exposure pooled-prevalence rate of 23% was estimated, with values of 24% calculated for depression and ⁓17% for both PTSD and anxiety. Notably, calculated pooled odds ratios (pOR = 1.9) indicate a high likelihood of any negative psychological outcome (+90%) following EWE exposure. Pooled analyses of reported risk factors (p \u3c 0.05) highlight the pronounced impacts of EWEs among individuals with higher levels of event exposure or experienced stressors (14.5%) and socio-demographic traits traditionally linked to vulnerable sub-populations, including female gender (10%), previous history (i.e., pre-event) of psychological impairment (5.5%), lower socio-economic status (5.5%), and a lower education level (5.2%). Inherent limitations associated with collating mental health data from populations exposed to EWEs, and key knowledge gaps in the field are highlighted. Study findings provide a robust evidence base for developing and implementing public health intervention strategies aimed at ameliorating the psychological impacts of extreme weather among exposed populations

Impacts of COVID-19 lockdown on private domestic groundwater sample numbers, E. coli presence and E. coli concentration across Ontario, January 2020–March 2021: An interrupted time-series analysis

Approximately 1.5 million individuals in Ontario are supplied by private water wells (private groundwater supplies). Unlike municipal supplies, private well water quality remains unregulated, with owners responsible for testing, treating, and maintaining their own water supplies. The COVID-19 global pandemic and associated non-pharmaceutical interventions (NPIs) have impacted many environmental (e.g., surface water and air quality) and human (e.g., healthcare, transportation) systems over the past 15-months (January 2020 to March 2021). To date, the impact of these interventions on private groundwater systems remains largely unknown. Accordingly, the current study aimed to investigate the impact of a province-wide COVID-19 lockdown (late-March 2020) on health behaviours (i.e., private domestic groundwater sampling) and groundwater quality (via Escherichia coli (E. coli) detection and concentration) in private well water in Ontario, using time-series analyses (seasonal decomposition, interrupted time-series) of a large-spatio-temporal dataset (January 2016 to March 2021; N = 743,200 samples). Findings indicate that lockdown concurred with an immediate (p = 0.015) and sustained (p \u3c 0.001) decrease in sampling rates, equating to approximately 2200 fewer samples received per week post-interruption. Likewise, a slightly decreased E. coli detection rate was observed approximately one month after lockdowns began (p = 0.003), while the proportion of “highly contaminated” samples (i.e., E. coli \u3e 10 CFU/100 mL) was shown to increase within one month (p = 0.02), followed by a sustained decrease for the remainder of the year (May 2020–December 2020). Analyses strongly suggest that COVID-19 interventions resulted in discernible impacts on both well user behaviours and hydrogeological mechanisms. Findings may be used as an evidence-base for assisting policy makers, public health practitioners and private well owners in developing recommendations and mitigation strategies to manage public health risks during extreme and/or unprecedented future events

Socio-Economic Factors Associated With The Incidence of Shiga-Toxin Producing Escherichia Coli (STEC) Enteritis and Cryptosporidiosis in the Republic of Ireland, 2008–2017

The Republic of Ireland (ROI) currently reports the highest incidence rates of Shiga-toxin producing Escherichia coli (STEC) enteritis and cryptosporidiosis in Europe, with the spatial distribution of both infections exhibiting a clear urban/rural divide. To date, no investigation of the role of socio-demographic profile on the incidence of either infection in the ROI has been undertaken. The current study employed bivariate analyses and Random Forest classification to identify associations between individual components of a national deprivation index and spatially aggregated cases of STEC enteritis and cryptosporidiosis. Classification accuracies ranged from 78.2% (STEC, urban) to 90.6% (cryptosporidiosis, rural). STEC incidence was (negatively) associated with a mean number of persons per room and percentage of local authority housing in both urban and rural areas, addition to lower levels of education in rural areas, while lower unemployment rates were associated with both infections, irrespective of settlement type. Lower levels of third-level education were associated with cryptosporidiosis in rural areas only. This study highlights settlement-specific disparities with respect to education, unemployment and household composition, associated with the incidence of enteric infection. Study findings may be employed for improved risk communication and surveillance to safeguard public health across socio-demographic profiles

Scavenger 0.1: A Theorem Prover Based on Conflict Resolution

This paper introduces Scavenger, the first theorem prover for pure first-order logic without equality based on the new conflict resolution calculus. Conflict resolution has a restricted resolution inference rule that resembles (a first-order generalization of) unit propagation as well as a rule for assuming decision literals and a rule for deriving new clauses by (a first-order generalization of) conflict-driven clause learning.Comment: Published at CADE 201

Mobility of lysozyme in poly(L-lysine)/hyaluronic acid multilayer films

The spatial and temporal control over presentation of protein-based biomolecules such as growth factors and hormones is crucial for in vitro applications to mimic the complex in vivo environment. We investigated the interaction of a model protein lysozyme (Lys) with poly(L-lysine)/hyaluronic acid (PLL/HA) multilayer films. We focused on Lys diffusion as well as adsorption and retention within the film as a function of the film deposition conditions and post-treatment. Additionally, an effect of Lys concentration on its mobility was probed. A combination of confocal fluorescence microscopy, fluorescence recovery after photobleaching, and microfluidics was employed for this investigation. Our main finding is that adsorption of PLL and HA after protein loading induces acceleration and reduction of Lys mobility, respectively. These results suggest that a charge balance in the film to a high extent governs the protein–film interaction. We believe that control over protein mobility is a key to reach the full potential of the PLL/HA films as reservoirs for biomolecules depending on the application demand